Polygenic Risk Score Task Force of the International Common Disease Alliance. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Can genetics guide exercise prescriptions in osteoarthritis?

Osteoarthritis (OA) is the most common form of arthritis and has a multifactorial etiology. Current management for OA focuses on minimizing pain and functional loss, typically involving pharmacological, physical, psychosocial, and mind-body interventions. However, there remain challenges in determining which patients will benefit most from which interventions. Although exercise-based interventions are recommended as first-line treatments and are known to be beneficial for managing both the disease and illness of OA, the optimal exercise “prescription” is unknown, due in part to our limited understanding of the precise mechanisms underlying its action. Here we present our perspective on the potential role of genetics in guiding exercise prescription for persons with OA. We describe key publications in the areas of exercise and OA, genetics and OA, and exercise and genetics, and point to a paucity of knowledge at the intersection of exercise, genetics, and OA. We suggest there is emerging evidence to support the use of genetics and epigenetics to explain the beneficial effects of exercise for OA. We identify missing links in the existing research relating to exercise, genetics, and OA, and highlight epigenetics as a promising mechanism through which environmental exposures such as exercise may impact OA outcomes. We anticipate future studies will improve our understanding of how genetic and epigenetic factors mediate exercise-based interventions to support implementation and ultimately improve OA patient care.

New Cardiovascular Risk Assessment Techniques for Primary Prevention: JACC Review Topic of the Week

Risk factor-based models fail to accurately estimate risk in select populations, in particular younger individuals. A sizable number of people are also classified as being at intermediate risk, for whom the optimal preventive strategy could be more precise. Several personalized risk prediction tools, including coronary artery calcium scoring, polygenic risk scores, and metabolic risk scores may be able to improve risk assessment, pending supportive outcome data from clinical trials. Other tools may well emerge in the near future. A multidimensional approach to risk prediction holds the promise of precise risk prediction. This could allow for targeted prevention minimizing unnecessary costs and risks while maximizing benefits. High-risk individuals could also be identified early in life, creating opportunities to arrest the development of nascent coronary atherosclerosis and prevent future clinical events.

Gynecologic Cancer Risk and Genetics: Informing an Ideal Model of Gynecologic Cancer Prevention

Individuals with proven hereditary cancer syndrome (HCS) such as BRCA1 and BRCA2 have elevated rates of ovarian, breast, and other cancers. If these high-risk people can be identified before a cancer is diagnosed, risk-reducing interventions are highly effective and can be lifesaving. Despite this evidence, the vast majority of Canadians with HCS are unaware of their risk. In response to this unmet opportunity for prevention, the British Columbia Gynecologic Cancer Initiative convened a research summit “Gynecologic Cancer Prevention: Thinking Big, Thinking Differently” in Vancouver, Canada on 26 November 2021. The aim of the conference was to explore how hereditary cancer prevention via population-based genetic testing could decrease morbidity and mortality from gynecologic cancer. The summit invited local, national, and international experts to (1) discuss how genetic testing could be more broadly implemented in a Canadian system, (2) identify key research priorities in this topic and (3) outline the core essential elements required for such a program to be successful. This report summarizes the findings from this research summit, describes the current state of hereditary genetic programs in Canada, and outlines incremental steps that can be taken to improve prevention for high-risk Canadians now while developing an organized population-based hereditary cancer strategy.

Translational impact of omics studies in alopecia areata: recent advances and future perspectives

INTRODUCTION

Alopecia areata (AA) is a non-scarring, hair loss disorder and a common autoimmune-mediated disease with an estimated lifetime risk of about 2%. To date, the treatment of AA is mainly based on suppression or stimulation of the immune response. Genomics and transcriptomics studies generated important insights into the underlying pathophysiology, enabled discovery of molecular disease signatures, which were used in some of the recent clinical trials to monitor drug response and substantiated the consideration of new therapeutic modalities for the treatment of AA such as abatacept, dupilumab, ustekinumab and Janus Kinase (JAK) inhibitors.

AREAS COVERED

In this review, genomics and transcriptomics studies in AA are discussed in detail with particular emphasis on their past and prospective translational impacts. Microbiome studies are also briefly introduced.

EXPERT OPINION

The generation of large datasets using the new high-throughput technologies has revolutionized medical research and AA has also benefited from the wave of omics studies. However, the limitations associated with JAK inhibitors and clinical heterogeneity in AA patients underscore the necessity for continuing omics research in AA for discovery of novel therapeutic modalities and development of clinical tools for precision medicine.

Why Do We Care More About Disease than Health?

Modern Western biomedical research and clinical practice are primarily focused on disease. This disease-centric approach has yielded an impressive amount of knowledge around what goes wrong in illness. However, in comparison, researchers and physicians know little about health. What is health? How do we quantify it? And how do we improve it? We currently do not have good answers to these questions. Our lack of fundamental knowledge about health is partly driven by three main factors: (i) a lack of understanding of the dynamic processes that cause variations in health/disease states over time, (ii) an excessive focus on genes, and (iii) a pervasive psychological bias towards additive solutions. Here I briefly discuss potential reasons why scientists and funders have generally adopted a gene- and disease-centric framework, how medicine has ended up practicing "diseasecare" rather than healthcare, and present cursory evidence that points towards an alternative energetic view of health. Understanding the basis of human health with a similar degree of precision that has been deployed towards mapping disease processes could bring us to a point where we can actively support and promote human health across the lifespan, before disease shows up on a scan or in bloodwork.

Perspectives of diverse Spanish- and English-speaking patients on the clinical use of polygenic risk scores

PURPOSE

As polygenic risk scores (PRS) emerge as promising tools to inform clinical care, there is a pressing need for patient-centered evidence to guide their implementation, particularly in diverse populations. Here, we conducted in-depth interviews of diverse Spanish- and English-speaking patients to explore their perspectives on clinical PRS.

METHODS

We enrolled 30 biobank participants aged 35-50 years through a purposive sampling strategy, ensuring that >75% self-reported as African/African American or Hispanic/Latinx and half were Spanish-speaking. Semistructured interviews in Spanish or English explored attitudes toward PRS, barriers to adoption, and communication preferences. Data were analyzed using an inductive thematic analysis approach.

RESULTS

Perceived utility of clinical PRS focused on the potential for personal health benefits, and most participants stated that high-risk results would prompt physician consultations and health behavior changes. There was little concern among participants about the limited predictive power of PRS for non-European populations. Barriers to uptake of PRS testing and adoption of PRS-related recommendations included socioeconomic factors, insurance status, race, ethnicity, language, and inadequate understanding of PRS. Participants favored in-person PRS result disclosure by their physician.

CONCLUSION

Findings provide valuable insight into diverse patients' attitudes and potential barriers related to clinical PRS, guiding future research and patient-centered clinical implementation.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

DNA methylation-based predictors of health: applications and statistical considerations

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

Genetics of Alzheimer Disease

PURPOSE OF REVIEW

This article discusses the spectrum of genetic risk in familial and sporadic forms of early- and late-onset Alzheimer disease (AD). Recent work illuminating the complex genetic architecture of AD is discussed in the context of high and low risk and what is known in different populations.

RECENT FINDINGS

A small proportion of AD is autosomal dominant familial AD caused by variants in PSEN1, PSEN2, or APP, although more recently described rare genetic changes can also increase risk substantially over the general population, with odds ratios estimated at 2 to 4. APOE remains the strongest genetic risk factor for late-onset AD, and understanding the biology of APOE has yielded mechanistic insights and leads for therapeutic interventions. Genome-wide studies enabled by rapidly developing technologic advances in sequencing have identified numerous risk factors that have a low impact on risk but are widely shared throughout the population and involve a repertoire of cell pathways, again shining light on potential paths to intervention. Population studies aimed at defining and stratifying genetic AD risk have been informative, although they are not yet widely applicable clinically because the studies were not performed in people with diverse ancestry and ethnicity and thus population-wide data are lacking.

SUMMARY

The value of genetic information to practitioners in the clinic is distinct from information sought by researchers looking to identify novel therapeutic targets. It is possible to envision a future in which genetic stratification joins other biomarkers to facilitate therapeutic choices and inform prognosis. Genetics already has transformed our understanding of AD pathogenesis and will, no doubt, continue to reveal the complexity of brain biology in health and disease.

Personalised Risk Prediction in Hereditary Breast and Ovarian Cancer: A Protocol for a Multi-Centre Randomised Controlled Trial

Women who test positive for an inherited pathogenic/likely pathogenic gene variant in BRCA1, BRCA2, PALB2, CHEK2 and ATM are at an increased risk of developing certain types of cancer-specifically breast (all) and epithelial ovarian cancer (only BRCA1, BRCA2, PALB2). Women receive broad cancer risk figures that are not personalised (e.g., 44-63% lifetime risk of breast cancer for those with PALB2). Broad, non-personalised risk estimates may be problematic for women when they are considering how to manage their risk. Multifactorial-risk-prediction tools have the potential to deliver personalised risk estimates. These may be useful in the patient's decision-making process and impact uptake of risk-management options. This randomised control trial (registration number to follow), based in genetic centres in the UK and US, will randomise participants on a 1:1 basis to either receive conventional cancer risk estimates, as per routine clinical practice, or to receive a personalised risk estimate. This personalised risk estimate will be calculated using the CanRisk risk prediction tool, which combines the patient's genetic result, family history and polygenic risk score (PRS), along with hormonal and lifestyle factors. Women's decision-making around risk management will be monitored using questionnaires, completed at baseline (pre-appointment) and follow-up (one, three and twelve months after receiving their risk assessment). The primary outcome for this study is the type and timing of risk management options (surveillance, chemoprevention, surgery) taken up over the course of the study (i.e., 12 months). The type of risk-management options planned to be taken up in the future (i.e., beyond the end of the study) and the potential impact of personalised risk estimates on women's psychosocial health will be collected as secondary-outcome measures. This study will also assess the acceptability, feasibility and cost-effectiveness of using personalised risk estimates in clinical care.

Reaching for Precision Healthcare in Finland via Use of Genomic Data

Concerns over future healthcare capacity along with continuing demands for sustainability call for novel solutions to improve citizens' health and wellbeing through effective prevention and improved diagnosis and treatment. Part of the solution to tackle the challenge could be making the most of the exploitation of genomic data in personalized risk assessment, creating new opportunities for data-driven precision prevention and public health. Presently, the utilization of genomic data in the Finnish healthcare system is limited to a few medical specialty areas. To successfully extend the use of genomic information in everyday healthcare, evidence-based and feasible strategies are needed. The national actions that Finland is taking towards this goal are 1) providing scientific evidence for the utility of genomic information for healthcare purposes; 2) evaluating the potential health-economic impact of implementing precision healthcare in Finland; 3) developing a relevant legal framework and infrastructures for the utilization of genomic information; 4) building a national multidisciplinary expert network bringing together relevant professionals and initiatives to achieve consensus among the different stakeholders on specific issues vital for translating genomic data into precision healthcare; 5) building competence and genomic literacy skills among various target groups; and 6) public engagement (informing and educating the public). Taken together, these actions will enable building a roadmap towards the expedient application of genomic data in Finnish healthcare and promoting the health of our citizens.

Investigating the feasibility and ethical implications of phenotypic screening using stem cell-derived tissue models to detect and manage disease

Stem-cell-derived tissue models generated from sick people are being used to understand human development and disease, drug development, and drug screening. However, it is possible to detect disease phenotypes before a patient displays symptoms, allowing for their use as a disease screening tool. This raises numerous issues, some of which can be addressed using similar approaches from genetic screenings, while others are unique. One issue is the relationship between disease disposition, biomarker detection, and patient symptoms and how tissue models could be used to define disease. Other issues include decisions of when to screen, what diseases to screen for, and what treatment options should be offered.

The translational challenges of precision oncology

The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.

Genomics and justice: mitigating the potential harms and inequities that arise from the implementation of genomics in medicine

Advances in human genetics raise many social and ethical issues. The application of genomic technologies to healthcare has raised many questions at the level of the individual and the family, about conflicts of interest among professionals, and about the limitations of genomic testing. In this paper, we attend to broader questions of social justice, such as how the implementation of genomics within healthcare could exacerbate pre-existing inequities or the discrimination against social groups. By anticipating these potential problems, we hope to minimise their impact. We group the issues to address into six categories: (i) access to healthcare in general, not specific to genetics. This ranges from healthcare insurance to personal behaviours. (ii) data management and societal discrimination against groups on the basis of genetics. (iii) epigenetics research recognises how early life exposure to stress, including malnutrition and social deprivation, can lead to ill health in adult life and further social disadvantage. (iv) psychiatric genomics and the genetics of IQ may address important questions of therapeutics but could also be used to disadvantage specific social or ethnic groups. (v) complex diseases are influenced by many factors, including genetic polymorphisms of individually small effect. A focus on these polygenic influences distracts from environmental factors that are more open to effective interventions. (vi) population genomic screening aims to support couples making decisions about reproduction. However, this remains a highly contentious area. We need to maintain a careful balance of the competing social and ethical tensions as the technology continues to develop.

Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Stroke Genetics: Discovery, Insight Into Mechanisms, and Clinical Perspectives

Stroke is the second leading cause of death worldwide and a complex, heterogeneous condition. In this review, we provide an overview of the current knowledge on monogenic and multifactorial forms of stroke, highlighting recent insight into the continuum between these. We describe how, in recent years, large-scale genome-wide association studies have enabled major progress in deciphering the genetic basis for stroke and its subtypes, although more research is needed to interpret these findings. We cover the potential of stroke genetics to reveal novel pathophysiological processes underlying stroke, to accelerate the discovery of new therapeutic approaches, and to identify individuals in the population who are at high risk of stroke and could be targeted for tailored preventative interventions.

The future of early cancer detection

A proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival. However, as detection methods become increasingly sensitive, it can be difficult to distinguish inconsequential changes from lesions that will lead to life-threatening cancer. Progress relies on a detailed understanding of individualized risk, clear delineation of cancer development stages, a range of testing methods with optimal performance characteristics, and robust evaluation of the implications for individuals and society. In the future, advances in sensors, contrast agents, molecular methods, and artificial intelligence will help detect cancer-specific signals in real time. To reduce the burden of cancer on society, risk-based detection and prevention needs to be cost effective and widely accessible.

The Road Traveled and Journey Ahead for the Genetics and Genomics of Tinnitus

The feasibility to unravel genetic and genomic signatures for disorders affecting the auditory system has accelerated since arriving in the post-genomics era roughly 20 years ago. Newly emerging studies have provided initial landmarks signaling heritability and thus, a genetic link, to severe tinnitus. Tinnitus, the phantom perception of ringing in the ears, is experienced by at least 15% of the adult population and can be extremely disabling. Despite its ubiquity, there is no cure for tinnitus and modalities offering relief are often of limited success. Because tinnitus is frequently reported in patients with acquired conductive or sensorineural hearing impairment, it has been widely accepted that tinnitus is secondary to and a symptom arising from hearing impairment. However, tinnitus has also been identified in the absence of auditory dysfunction and in young individuals, resulting in a debate about its origins. Genetics studies have identified severe tinnitus as a complex disorder arising from gene and environment interactions, refining its classification as a neurological disorder and, in at least a subset of patients, it appears not as a symptom of another health issue. This current opinion summarizes several recent studies that have challenged a long-accepted dogma and postulates how this information could eventually be used in the future to help patients. It is with great hope that this knowledge opens translational paths to provide relief for the many who suffer from the burden of tinnitus on a daily basis.

Use of 'omics for endometrial timing: the cycle moves on

For some years, the prospect of precise and personalized timing of the endometrial cycle for optimal embryo replacement has been held out as a potential solution to low implantation rates. It is envisaged that a receptive state can be defined and reached at a predictable time, and embryo replacement performed in synchrony. In the last century, morphological changes characteristic of the mid secretory phase were defined in precisely timed cycles in women of proven fertility, but when deviations from this standardized schedule occur, their significance for implantation has remained uncertain. ‘Omics technologies have been widely advocated for staging the endometrial cycle and defining a set of biochemical requirements for implantation, but after two decades of research, improvements to pregnancy rates have not followed, and there is a striking lack of agreement regarding the molecular characterization of the receptive state. Some of the rationale underlying these problems is now emerging with the application of higher-level computational and biological methodology. Here, we consider the challenges of defining an endometrial phenotype that can support implantation and continuing pregnancy. Receptivity may be an emergent trait depending on contributions from multiple proteins that have low pathway connectivity. We recommend that authors choose language which rigorously avoids the implication that protocols for molecular staging of the mid secretory phase inherently identify a state of receptivity to the implanting blastocyst.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

Genetic risk score to improve prediction and treatment in gestational diabetes mellitus

Diabetes mellitus is a chronic disease caused by the interaction of genetics and the environment that can lead to chronic damage to many organ systems. Genome-wide association studies have identified accumulating single-nucleotide polymorphisms related to type 2 diabetes mellitus and gestational diabetes mellitus. Genetic risk score (GRS) has been utilized to evaluate the incidence risk to improve prediction and optimize treatments. This article reviews the research progress in the use of the GRS in diabetes mellitus in recent years and discusses future prospects.

Evaluating the impact of a new educational tool on understanding of polygenic risk scores for alcohol use disorder

INTRODUCTION

As gene identification efforts have advanced in psychiatry, so have aspirations to use genome-wide polygenic information for prevention and intervention. Although polygenic risk scores (PRS) for substance use and psychiatric outcomes are not yet available in clinical settings, individuals can access their PRS through online direct-to-consumer resources. One of these widely used websites reports that alcohol use disorder is the third most requested PRS out of >1,000 conditions. However, data indicate that there are misunderstandings about complex genetic concepts, with a lower understanding of PRS being associated with a more negative impact of receiving polygenic risk information. There is a need to develop and evaluate educational tools to increase understanding of PRS.

METHODS

We conducted a randomized controlled trial to evaluate the impact of web-based educational information on understanding of PRS for alcohol use disorder. A total of 325 college students (70.4% female; 43.6% White; mean age = 18.9 years) from an urban, diverse university completed the study.

RESULTS

Overall, participants were highly satisfied with the educational information. Results from a one-way ANOVA indicated that there was a significant increase in overall understanding of PRS for alcohol use disorder (p-value < 0.001), among individuals who received educational information about PRS and alcohol use disorder, as compared to receiving no accompanying information (adj. p-value < 0.001), or educational information about alcohol use disorder only (adj. p-value < 0.001).

DISCUSSION

These findings suggest that the web-based educational tool could be provided alongside polygenic risk information in order to enhance understanding and interpretation of the information.

CLINICAL TRIAL REGISTRATION

[ClinicalTrials.gov], identifier [NCT05143073].

LDL-C Concentrations and the 12-SNP LDL-C Score for Polygenic Hypercholesterolaemia in Self-Reported South Asian, Black and Caribbean Participants of the UK Biobank

Background: Monogenic familial hypercholesterolaemia (FH) is an autosomal dominant disorder characterised by elevated low-density lipoprotein cholesterol (LDL-C) concentrations due to monogenic mutations in LDLR, APOB, PCSK9, and APOE. Some mutation-negative patients have a polygenic cause for elevated LDL-C due to a burden of common LDL-C-raising alleles, as demonstrated in people of White British (WB) ancestry using a 12-single nucleotide polymorphism (SNP) score. This score has yet to be evaluated in people of South Asian (SA), and Black and Caribbean (BC) ethnicities. Objectives: 1) Compare the LDL-C and 12-SNP score distributions across the three major ethnic groups in the United Kingdom: WB, SA, and BC individuals; 2) compare the association of the 12-SNP score with LDL-C in these groups; 3) evaluate ethnicity-specific and WB 12-SNP score decile cut-off values, applied to SA and BC ethnicities, in predicting LDL-C concentrations and hypercholesterolaemia (LDL-C>4.9 mmol/L). Methods: The United Kingdom Biobank cohort was used to analyse the LDL-C (adjusted for statin use) and 12-SNP score distributions in self-reported WB (n = 353,166), SA (n = 7,016), and BC (n = 7,082) participants. To evaluate WB and ethnicity-specific 12-SNP score deciles, the total dataset was split 50:50 into a training and testing dataset. Regression analyses (logistic and linear) were used to analyse hypercholesterolaemia (LDL-C>4.9 mmol/L) and LDL-C. Findings: The mean (±SD) measured LDL-C differed significantly between the ethnic groups and was highest in WB [3.73 (±0.85) mmol/L], followed by SA [3.57 (±0.86) mmol/L, p < 2.2 × 10^-16], and BC [3.42 (±0.90) mmol/L] participants (p < 2.2 × 10^-16). There were significant differences in the mean (±SD) 12-SNP score between WB [0.90 (±0.23)] and BC [0.72 (±0.25), p < 2.2 × 10^-16], and WB and SA participants [0.86 (±0.19), p < 2.2 × 10^-16]. In all three ethnic groups the 12-SNP score was associated with measured LDL-C [R ² (95% CI): WB = 0.067 (0.065-0.069), BC = 0.080 (0.063-0.097), SA = 0.027 (0.016-0.038)]. The odds ratio and the area under the curve for hypercholesterolaemia were not statistically different when applying ethnicity-specific or WB deciles in all ethnic groups. Interpretation: We provide information on the differences in LDL-C and the 12-SNP score distributions in self-reported WB, SA, and BC individuals of the United Kingdom Biobank. We report the association between the 12-SNP score and LDL-C in these ethnic groups. We evaluate the performance of ethnicity-specific and WB 12-SNP score deciles in predicting LDL-C and hypercholesterolaemia.

Polygenic Scores of Alzheimer's Disease Risk Genes Add Only Modestly to APOE in Explaining Variation in Amyloid PET Burden

BACKGROUND

Brain accumulation of amyloid-β is a hallmark event in Alzheimer's disease (AD) whose underlying mechanisms are incompletely understood. Case-control genome-wide association studies have implicated numerous genetic variants in risk of clinically diagnosed AD dementia.

OBJECTIVE

To test for associations between case-control AD risk variants and amyloid PET burden in older adults, and to assess whether a polygenic measure encompassing these factors would account for a large proportion of the unexplained variance in amyloid PET levels in the wider population.

METHODS

We analyzed data from the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Global cortical amyloid PET burden was the primary outcome. The 38 gene variants from Wightman et al. (2021) were analyzed as predictors, with PRSice-2 used to assess the collective phenotypic variance explained.

RESULTS

Known AD risk variants in APOE, PICALM, CR1, and CLU were associated with amyloid PET levels. In aggregate, the AD risk variants were strongly associated with amyloid PET levels in the MCSA (p = 1.51×10-50) and ADNI (p = 3.21×10-64). However, in both cohorts the non-APOE variants uniquely contributed only modestly (MCSA = 2.1%, ADNI = 4.4%) to explaining variation in amyloid PET levels.

CONCLUSION

Additional case-control AD risk variants added only modestly to APOE in accounting for individual variation in amyloid PET burden, results which were consistent across independent cohorts with distinct recruitment strategies and subject characteristics. Our findings suggest that advancing precision medicine for dementia may require integration of strategies complementing case-control approaches, including biomarker-specific genetic associations, gene-by-environment interactions, and markers of disease progression and heterogeneity.